Papers › Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching

Offline Imitation from Observation via Primal Wasserstein State Occupancy Matching

2 Nov 2023arXiv:2311.01331archive 2025-07-28

Kai Yan, Alexander G. Schwing, Yu-Xiong Wang

In real-world scenarios, arbitrary interactions with the environment can often be costly, and actions of expert demonstrations are not always available. To reduce the need for both, offline Learning from Observations (LfO) is extensively studied: the agent learns to solve a task given only expert states and task-agnostic non-expert state-action pairs. The state-of-the-art DIstribution Correction Estimation (DICE) methods, as exemplified by SMODICE, minimize the state occupancy divergence between the learner's and empirical expert policies. However, such methods are limited to either f-divergences (KL and chi²) or Wasserstein distance with Rubinstein duality, the latter of which constrains the underlying distance metric crucial to the performance of Wasserstein-based solutions. To enable more flexible distance metrics, we propose Primal Wasserstein DICE (PW-DICE). It minimizes the primal Wasserstein distance between the learner and expert state occupancies and leverages a contrastively learned distance metric. Theoretically, our framework is a generalization of SMODICE, and is the first work that unifies f-divergence and Wasserstein minimization. Empirically, we find that PW-DICE improves upon several state-of-the-art methods. The code is available at https://github.com/KaiYan289/PW-DICE.

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add_terminals KaiYan289/PW-DICE/PWDICE_continuous/dataset.py official repository ran no licence file found · pointer only · 7ce1439208658937 · report
atanh KaiYan289/PW-DICE/PWDICE_continuous/NN.py official repository ran · our draft was wrong fingerprinted no licence file found · pointer only · cf5ce98955ab4534 · report
choice KaiYan289/PW-DICE/PWDICE_continuous/advance_NN.py official repository ran no licence file found · pointer only · c8bdd33928f69383 · report
choice2 KaiYan289/PW-DICE/PWDICE_continuous/advance_NN.py official repository ran no licence file found · pointer only · 347766442faf6236 · report
concatlist kaiyan289/pw-dice/PWDICE_tabular_wrapup/ours_newKL_solver_arbitrary.py official repository ran · honoured contract no licence file found · pointer only · 3665ac63b6a49f6e · report
construct_dataset KaiYan289/PW-DICE/PWDICE_continuous/dataset.py official repository ran no licence file found · pointer only · 0e03fe35d8e01f02 · report
get_data KaiYan289/PW-DICE/PWDICE_continuous/datagen.py official repository ran no licence file found · pointer only · 333cd5157d8a351c · report
get_data_pre KaiYan289/PW-DICE/PWDICE_continuous/datagen.py official repository ran no licence file found · pointer only · ac35521adf325e83 · report
get_dataset KaiYan289/PW-DICE/PWDICE_continuous/dataset.py official repository ran no licence file found · pointer only · 2b68a6cee77f95fb · report
get_dist KaiYan289/PW-DICE/PWDICE_continuous/dist_metric.py official repository ran no licence file found · pointer only · ec8e6f427087880b · report
list2gen kaiyan289/pw-dice/PWDICE_tabular_wrapup/ours_newKL_solver_arbitrary.py official repository ran · our draft was wrong no licence file found · pointer only · 03cb6d46061c329e · report
soft_linear_piecewise_loss KaiYan289/PW-DICE/PWDICE_continuous/dist_metric.py official repository ran no licence file found · pointer only · 2331e1c5de71901c · report
estimate_traj_val KaiYan289/PW-DICE/PWDICE_continuous/advance_NN.py official repository unverified no licence file found · pointer only · b1a53ce39d2e8d5c · report
git_commit kaiyan289/pw-dice/PWDICE_tabular_wrapup/ours_newKL_solver_arbitrary.py official repository unverified no licence file found · pointer only · a5206fc3db71b6bf · report

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